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Chapter 27 : Phenomics

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Abstract:

This chapter focuses on types of phenotypic measurements, technological advances that enable high-throughput phenotypic measurements, computational tools that are used to interpret and predict phenotypic behavior, and the impact that the field of phenomics will have on biotechnology. Phenomics looks to elucidate the genotype-phenotype relationship; as such, current work is primarily limited to studying organisms with a well characterized genotype. The current state of phenomics is focused on studying behaviors that are typical of simpler organisms. The growth properties of a microorganism can now be assessed in a high-throughput manner. Largely because of the newness of the field, the number of high-throughput technologies useful for phenomics is limited. Most tools that are particularly useful for phenomics are focused on measuring growth capabilities of microorganisms in a high-throughput manner. In the near future, one should expect the development of new technologies that will help propel the field of phenomics forward. Genome-scale models of metabolic networks are needed to interpret and predict phenomic data. So far, these genome-scale models have only been analyzed by using flux-balance analysis (FBA) and extreme pathway analysis, mainly due to a lack of measured enzymatic parameters that are needed in some of the other modeling methodologies. For this reason the principles behind FBA and its uses are discussed, and readers are encouraged to access the given references for descriptions of the other modeling methodologies. Finally, the chapter discusses two examples illustrating how the field of phenomics will affect biotechnology.

Citation: Reed J, Fong S, Palsson B. 2004. Phenomics, p 280-287. In Bull A (ed), Microbial Diversity and Bioprospecting. ASM Press, Washington, DC. doi: 10.1128/9781555817770.ch27

Key Concept Ranking

Saccharomyces cerevisiae
0.52272725
Escherichia coli
0.4575208
Helicobacter pylori
0.4575208
Escherichia coli
0.4575208
Helicobacter pylori
0.4575208
0.52272725
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Figures

Image of Figure 1
Figure 1

The classification of “omics” data. Genomics involves the study of an organism's genome, including sequencing of its open reading frame, identification, and annotation. Functional genomics is an area of genomics that involves the functional assignment of genes. As diagrammed, the three proteins encoded by genes 1, 2, and 3 are subunits of a functional enzyme that catalyzes the conversion of metabolites A and B into C. Phenomics is the study of phenotypes, and this information is used to understand the genotype-phenotype relationship, which is also dependent on environmental conditions.

Citation: Reed J, Fong S, Palsson B. 2004. Phenomics, p 280-287. In Bull A (ed), Microbial Diversity and Bioprospecting. ASM Press, Washington, DC. doi: 10.1128/9781555817770.ch27
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Image of Figure 2
Figure 2

Phenotypic measurements. There are four basic types (boxed in gray) of phenotypic measurements that can be made. Nutrient uptake rates can be measured, which would include carbon source (in this case glucose), oxygen, nitrogen, sulfur, and phosphate uptake rates. Growth rate (µ) can be measured with high-throughput technology. By-product secretion, including identification of the compounds and measurement of the secretion rates, can be evaluated. By using radioactively labeled substrates, internal fluxes can also be measured.

Citation: Reed J, Fong S, Palsson B. 2004. Phenomics, p 280-287. In Bull A (ed), Microbial Diversity and Bioprospecting. ASM Press, Washington, DC. doi: 10.1128/9781555817770.ch27
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Figure 3

Steps involved in FBA. A metabolic genotype can be used to define a metabolic network, shown at upper right. The range of possible flux values through the individual reactions in the network lie in a high-dimensional flux space. A schematic representation of a three-dimensional projection of this high-dimensional space is shown on the left. Note that the solution spaces on the left are not specific for the actual network depicted on the right. The individual flux values in this network without any applied constraints can be any real number, and this is represented by an unconstrained solution space, shown in the upper left. Application of constraints (shown in the middle right diagram), including mass-balance, flux capacity, and thermodynamic constraints, leads to a convex cone that is the allowable solution space (shown in the middle left diagram) and contains all phenotypes that obey the applied constraints. The second step in FBA involves using linear optimization to select one solution (or phenotype) from the allowable solution space that optimizes a selected objective. In this case, the production of G was used as the objective. Optimality is illustrated by a point in the allowable solution space (shown in the lower left) or as a set of flux values through the network (shown in the lower right).

Citation: Reed J, Fong S, Palsson B. 2004. Phenomics, p 280-287. In Bull A (ed), Microbial Diversity and Bioprospecting. ASM Press, Washington, DC. doi: 10.1128/9781555817770.ch27
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Figure 4

PhPP varying succinate and oxygen uptake rates. Four regions emerge from the PhPP analysis, labeled I through 4. The areas to the left of region 1 and below region 4 are conditions in which the model does not predict growth, and they are labeled as such. The line of optimality separates regions 1 and 2. It corresponds to conditions that result in the highest biomass yield (with respect to succinate) and can easily be identified by fixing one flux and allowing the other to vary and then calculating the optimal solution. Expérimental studies (inset) found that fully aerobic growth operates around the line of optimality (A) whereas deviations from the line led to acetate secretion (B).

Citation: Reed J, Fong S, Palsson B. 2004. Phenomics, p 280-287. In Bull A (ed), Microbial Diversity and Bioprospecting. ASM Press, Washington, DC. doi: 10.1128/9781555817770.ch27
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Figure 5

Tracking adaptive evolution of on the glycerol-oxygen PhPP. The glycerol-oxygen PhPP for has six distinct regions. Initial experiments with K-12 MG1655 show that the strain does not operate optimally, the experimental points lie off the line of optimality. Using growth rate as a selective pressure, was grown in serial batch culture for 40 days. Two independent evolutionary trajectories are shown as dashed lines. In both cases, the growth rates doubled as compared to the initial starting point (day 0). Further testing of the endpoint strains (day 40) showed that they remained on the line of optimality, and the growth rate stabilized.

Citation: Reed J, Fong S, Palsson B. 2004. Phenomics, p 280-287. In Bull A (ed), Microbial Diversity and Bioprospecting. ASM Press, Washington, DC. doi: 10.1128/9781555817770.ch27
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